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https://github.com/BatsResearch/bonito
A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
https://github.com/BatsResearch/bonito
domain-adaptation gpt llm synthetic-data synthetic-dataset-generation task-adaptation zero-shot-learning
Last synced: 7 days ago
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A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
- Host: GitHub
- URL: https://github.com/BatsResearch/bonito
- Owner: BatsResearch
- License: bsd-3-clause
- Created: 2024-02-25T13:33:33.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-06-25T14:03:33.000Z (16 days ago)
- Last Synced: 2024-06-25T15:39:51.925Z (16 days ago)
- Topics: domain-adaptation, gpt, llm, synthetic-data, synthetic-dataset-generation, task-adaptation, zero-shot-learning
- Language: Python
- Homepage:
- Size: 781 KB
- Stars: 567
- Watchers: 13
- Forks: 38
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-stars - BatsResearch/bonito - A lightweight library for generating synthetic instruction tuning datasets for your data without GPT. (llm)
README
# Bonito
Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. This repo is a lightweight library for Bonito to easily create synthetic datasets built on top of the Hugging Face `transformers` and `vllm` libraries.
- Paper: [Learning to Generate Instruction Tuning Datasets for
Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
- Model: [bonito-v1](https://huggingface.co/BatsResearch/bonito-v1)
- Dataset: [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1)
- Code: To reproduce experiments in our paper, see [nayak-aclfindings24-code](https://github.com/BatsResearch/nayak-aclfindings24-code).
## News
- 🐟 June 2024: Bonito is accepted to ACL Findings 2024.
## Installation
Create an environment and install the package using the following commands:
```bash
conda create -n bonito python=3.9
conda activate bonito
pip install -e .
```
## Basic Usage
To generate synthetic instruction tuning dataset using Bonito, you can use the following code:
```python
from bonito import Bonito
from vllm import SamplingParams
from datasets import load_dataset
# Initialize the Bonito model
bonito = Bonito("BatsResearch/bonito-v1")
# load dataset with unannotated text
unannotated_text = load_dataset(
"BatsResearch/bonito-experiment",
"unannotated_contract_nli"
)["train"].select(range(10))
# Generate synthetic instruction tuning dataset
sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
synthetic_dataset = bonito.generate_tasks(
unannotated_text,
context_col="input",
task_type="nli",
sampling_params=sampling_params
)
```
## Supported Task Types
Here we include the supported task types [full name (short form)]: `extractive question answering` (`exqa`), `multiple-choice question answering` (`mcqa`), `question generation` (`qg`), `question answering without choices` (`qa`), `yes-no question answering` (`ynqa`), `coreference resolution` (`coref`), `paraphrase generation` (`paraphrase`), `paraphrase identification` (`paraphrase_id`), `sentence completion` (`sent_comp`), `sentiment` (`sentiment`), `summarization` (`summarization`), `text generation` (`text_gen`), `topic classification` (`topic_class`), `word sense disambiguation` (`wsd`), `textual entailment` (`te`), `natural language inference` (`nli`)
You can use either the full name or the short form to specify the `task_type` in `generate_tasks`.
## Tutorial
We have created a tutorial [here](https://colab.research.google.com/drive/12OCh4OYo1vr9ZvwIWK4JwZT7rkMrYrx2?usp=sharing) for how to use a quantized version of the model in a Google Colab T4 instance. The quantized version was graciously contributed by user [alexandreteles](https://github.com/alexandreteles).
We have an additional tutorial to try out the Bonito model on A100 GPU on Google Colab [here](https://colab.research.google.com/drive/1XuDRVKpUUqdjrqg2-P2FIqkdAQBnqoNL?usp=sharing).
## Citation
If you use Bonito in your research, please cite the following paper:
```
@inproceedings{bonito:aclfindings24,
title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
year = {2024}}
```